Online monitoring method of nuclear power plant system based on isolation forest method and sliding window method

The present disclosure relates to an online monitoring method of a nuclear power plant system based on an isolation forest method and a sliding window method. An isolation forest method used in the present disclosure is an abnormal detection model based on the idea of binary tree division, and has no requirements on the dimension and linear characteristics of monitoring data. In view of the characteristics of strong nonlinearity and high dimension of operation data of the nuclear power plant system, in the process of state monitoring, system abnormalities can be detected more quickly and accurately. In the present disclosure, a sliding window method is used to improve an isolation forest model, so that the improved isolation forest model has the functions of model online updating and real-time state monitoring, and the usability of an isolation forest state monitoring method is enhanced.